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Data Integration and the Semantic Interoperability Barrier: Business Impact and Practical Solutions - Webinar by EITAGlobal

Conference Date:

2013-10-10 (Archive)

Date Submitted:

2013-09-13

Announcement ID:

206590

Overview: In an economy where a company's business network of suppliers, distributors, partners, and customers is an increasingly important source of competitive advantage, semantic interoperability - the ability of human and automated agents to coordinate their functioning based on a shared understanding of the data that flows among them - is a major economic enabler. Consequently, semantic interoperability problems drive up integration costs across industry.

An integration analyst takes on the task of mapping one concrete data structure to another, which has to be done all too frequently when integrating systems. The data structures that the analyst must map are often lengthy and complicated, containing many data elements. Current state-of-the-art data mapping tools display both data structures on the screen and allow the analyst to graphically draw connections and write expressions to specify what should map to what according to what rules. These tools take the graphical map as input and produce an executable transformation, in keeping with a model-driven approach that is a genuine advance over having to write transformation programs in lower-level code as per the predominant practice of a decade ago. Thus, once the analyst has figured out what the mapping should be and has entered the mapping into the tool, the tool does useful things.

However, it is very time consuming and error prone for the analyst to figure out what should map to what. The integration tools lack the ability to help the analyst figure out what the mapping should be. Consequently, bottlenecks delay project implementations and subtle mistakes occur in data transformations used to integrate systems, costing the involved parties serious money. Even if the analyst has access to good documentation of the data structures, their size and complexity means the process is fraught with opportunities for delays and mistakes.

In sum, once the analyst has decided what should map to what, current-generation data integration tools simplify the mechanics of describing the mapping and getting it into executable form, and that is really a big help, but it's not sufficient, because if the analyst makes the wrong decision then the tools will simply make it possible to execute the wrong decision quickly. This is the essence of the semantic interoperability problem. The semantic interoperability problem not only causes delays and errors; it also stops projects from happening altogether. Parties often shy away from initiating a project that could add value in a market segment because high up-front integration costs stretch the return-on-investment horizon so far in the future that the project is not economically viable.

Why should you attend: The semantic interoperability problem is so large that it can be difficult to know where to begin when seeking to mitigate it. CIOs generally estimate that integration costs eat up 30 to 60 percent of their IT budgets. The good news is that the size of the problem means that we don't have to solve it completely in order to have substantial positive impact. Visa conducted an econometric study that suggested that modest improvements in semantic interoperability have the potential to measurably increase global GDP.

This seminar describes techniques for improving semantic interoperability that are starting to make their way into industry. It places an emphasis on the approaches that are relatively simple to implement and yet provide real business value. It also places those first steps on a path whose next steps are also incremental yet valuable.

Areas Covered in the Session:
 The current state of the art in data integration - what we have achieved, and what remains to be tackled
 The business impact of the lack of semantic interoperability on data integration complexity and costs
 New techniques for improving semantic interoperability, leveraging international standards
 How the new techniques are being incorporated into key finance and business reporting standards to which the presenter has been a major contributor
 Avoiding unnecessary disruption to the traditional data modeling process
 How the techniques leverage business vocabularies
 The relationship of the techniques to semantic technology such as the Semantic Web
 Applying the techniques to service-oriented architectures
 Applying the techniques to big data
 Keeping it simple: Identifying the approaches that are most achievable in the short to medium term and yet provide substantial benefit

Speaker Profile:
David Frankel has over 30 years of experience as a programmer, architect, and technical strategist. He is recognized as a pioneer and international authority on the subject of model-driven systems and semantic information modeling. He has a wealth of experience driving companies and industry at large to successfully adopt strategic technologies.

He has an outstanding ability to communicate in technical mentoring situations, publications, and presentations. He has published two books and dozens of trade press articles, and has presented at many industry conferences. He has a reputation for facilitating collaboration that, along with his strong technical expertise, has led him to co-author a number of important industry software standards, including UML®, ISO 20022, BIAN, and the XBRL Abstract Model. He served terms as an elected member of the Object Management Groups Architecture Board and Board of Directors.

The IT domains in which he has expertise include data integration, domain-specific languages, enterprise architecture, model-driven systems, semantic information modeling, semantic interoperability, service-oriented architecture, and software product lines. The business domains in which he has applied his technical expertise in recent years include financial services, ERP financials, and business reporting.

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